The subject of this thesis is the development of parametric methods for the calibration of array shape errors. Two physical scenarios are considered, the online calibration (self-calibration) using far-field sources and the offline calibration using near-field sources. The maximum likelihood (ML) estimators are employed to estimate the errors. However, the well-known computational complexity in objective function optimization for the ML estimators demands effective and efficient optimization algorithms. A novel space-alternating generalized expectation-maximization (SAGE)-based algorithm is developed to optimize the objective function of the conditional maximum likelihood (CML) estimator for the far-field online calibration. Through data augmentation, joint direction of arrival (DOA) estimation and array calibration can be carried out by a computationally simple search procedure. Numerical experiments show that the proposed method outperforms the existing method for closely located signal sources and is robust to large shape errors. In addition, the accuracy of the proposed procedure attains the Cram´er-Rao bound (CRB). A global optimization algorithm, particle swarm optimization (PSO) is employed to optimize the objective function of the unconditional maximum likelihood (UML) estimator for the farfield online calibration and the near-field offline calibration. A new technique, decaying diagonal loading (DDL) is proposed to enhance the performance of PSO at high signal-to-noise ratio (SNR) by dynamically lowering it, based on the counter-intuitive observation that the global optimum of the UML objective function is more prominent at lower SNR. Numerical simulations demonstrate that the UML estimator optimized by PSO with DDL is optimally accurate, robust to large shape errors, and free of the initialization problem. In addition, the DDL technique is applicable to a wide range of array processing problems where the UML estimator is employed and can be coupled with different global optimization algorithms.

An antenna array is an indispensable portion of a direction-of-arrival (DOA) estimation operation. A number of error sources in the arrays degrade the DOA estimation accuracy. Mutual coupling effect is one of the main error sources and should be corrected for any antenna array. In this thesis, a system theoretic approach is presented for mutual coupling characterization of antenna arrays. In this approach, the idea is to model the mutual coupling effect through a simple linear transformation between the measured and the ideal array data. In this context, a measurement reduction method (MRM) is proposed to decrease the number of calibration measurements. This new method dramatically reduces the number of calibration measurements for omnidirectional antennas. It is shown that a single calibration measurement is sufficient for uniform circular arrays when MRM is used. The method is extended for the arrays composed of non-omnidirectional (NOD) antennas. It is shown that a single calibration matrix can not properly model the mutual coupling effect in an NOD antenna array. Therefore, a sectorized calibration approach is proposed for NOD antenna arrays where the mutual coupling calibration is done in angular sectors. Furthermore, mutual coupling problem is also investigated for antenna arrays over a perfect electric conductor plate. In this case, reflections from the plate lead to gain/phase mismatches in the antenna elements. In this context, a composite matrix approach is proposed where mutual coupling and gain/phase mismatch are jointly modelled by using a single composite calibration matrix. The proposed methods are evaluated over DOA estimation accuracies using Multiple Signal Classification (MUSIC) algorithm. The calibration measurements are obtained using the numerical electromagnetic simulation tool FEKO. The evaluation results show that the proposed methods effectively realize the mutual coupling calibration of antenna arrays.

The speech of a person speaking in a noisy environment can be enhanced through electronic beamforming using spatially distributed microphones. As this approach demands precise information about the microphone locations, its application is limited in places where microphones must be placed quickly or changed on a regular basis. Highly precise calibration or measurement process can be tedious and time consuming. In order to understand tolerable limits on the calibration process, the impact of microphone position error on the intelligibility is examined. Analytical expressions are derived by modeling the microphone position errors as a zero mean uniform distribution. Experiments and simulations were performed to show relationships between precision of the microphone location measurement and loss in intelligibility. A variety of microphone array configurations and distracting sources (other interfering speech and white noise) are considered. For speech near the threshold of intelligibility, the results show that microphone position errors with standard deviations less than 1.5cm can limit losses in intelligibility to within 10% of the maximum (perfect microphone placement) for all the microphone distributions examined. Of different array distributions experimented, the linear array tends to be more vulnerable whereas the non-uniform 3D array showed a robust performance to positional errors.

For a linear array, the excitation coefficients of each element and its geometry play an important role, because they will determine the radiation pattern of the given array. Side Lobe Level (SLL) is one of the key parameters to evaluate the radiation pattern of the array. Generally speaking, we desire SLL to be as low as possible. For the linear array with uniform spacing, there are some classic methods to calculate the excitation coefficients to make the radiation pattern satisfy the given requirements. For the linear array with non-uniform spacing, linear programming and particle swarm optimization are proposed to calculate the excitation coefficients to make the array get minimum SLL in this thesis. They are demonstrated for symmetric and asymmetric array in the first part of this thesis. In the second part of this thesis, a simple method is proposed for correcting excitation coefficients of a linear phased array. This proposed method corrects the coefficients through using the Normalized Least Means Squares(NLMS) algorithm, dither signal and a near-field sensor being used for sensing the field emitted by the array. The advantage of this proposed method is that it avoids the problem of estimating the largest eigenvalue of the coefficient matrix to get optimal step size. Its robustness in different environments is demonstrated as well as the effect of noise with various Signal-to-Noise Ratio (SNR), and mutual coupling. In addition, the effect of using discrete dither signal to the array is considered, because the continuous dither signal cannot be generated in practice.

Radio frequency interference is an ever growing problem in the wireless community. This dissertation presents methods to reduce interference for vehicular multi-antenna devices. This document is organized into two parts: the main chapters and the appendices. The main chapters present research conducted primarily by the author. These deserve the reader's primary attention. The appendices showcase contributions made by the author serving in a supporting role to projects led by others and/or do not fit the vehicular theme. These should receive secondary attention. The main chapter contributions are summarized as follows. A device was created that provides over 105 dB of transmit to receive isolation in a full duplex printed circuit board radio. This technology can improve the effective range of vehicular radar systems and increase the bandwidth of full duplex communication schemes for vehicles. The technologies involved are compatible with existing circuit board topologies and are mindful of the size and weight requirements for vehicular use. This isolation performance pushes the state of the art for printed circuit board designs and provides greater capability for these kinds of devices. Recent system on chip computing architectures are opening new pathways for integrating phased array technologies into a single chip. The computer engineering required to configure these devices is beyond the capabilities of many vehicle systems engineers, inviting the author to use one to implement a 16 antenna adaptive beamformer for GPS. The adaptive beamformer can combat multipath bounces and malicious spoofing from ground sources. The high rate analog conversion architecture eliminates the local oscillator distribution to simplify the analog front end to an active antenna. This allows vehicular phased arrays to use smaller footprints and suggests that multi-antenna beamforming devices may be easier to deploy on small to midsized vehicles. Bench tests of the beamformer indicate it can adapt to the environment and increase the received signal strength suggesting it can improve GPS quality for active deployments. The bank of subspace projection beamformers is a popular choice for mitigating interference in digital phased array receivers. A method was discovered that maps that matrix operator into a circuit topology that is simple to implement in an analog circuit and cancels across the entire bandwidth simultaneously. This can offload computational interference mitigation from the signal processor while still allowing secondary multi-pixel digital beamforming downstream. This beamformer was analytically connected to the body of phased array literature and studied to estimate practical error bounds and design methods of calibration.

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Accurate indoor positioning of narrowband Internet-of-Things (IoT) sensors has drawn more attention in recent years. The introduction of Bluetooth Low Energy (BLE) technology is one of the latest developments of IoT and especially applicable for Ultra-Low Power (ULP) applications. BLE is an attractive technology for indoor positioning systems because of its low-cost deployment and reasonable accuracy. Efficient indoor positioning can be achieved by deducing the sensor position from the estimated signal Angle-of-Arrival (AoA) at multiple anchors. An anchor is a base station of known position and equipped with a narrowband multi-antenna array. However, the design and implementation of indoor positioning systems based on AoA measurements involve multiple challenges. The first part of this thesis mainly addresses the impact of hardware impairments on the accuracy of AoA measurements. In practice, the subspace-based algorithms such as Multiple Signal Classification (MUSIC) suffer from sensitivity to array calibration errors coming from hardware imperfections. A detailed experimental implementation is performed using a Software Defined Radio (SDR) platform to precisely evaluate the accuracy of AoA measurements. For this purpose, a new Over-the-Air (OTA) calibration method is proposed and the array calibration error is investigated. The experimental results are compared with the theoretical analysis. These results show that array calibration errors can cause some degrees of uncertainty in AoA estimation. Moreover, we propose iterative positioning algorithms based on AoA measurements for low capacity IoT sensors with high accuracy and fair computational complexity. Efficient positioning accuracy is obtained by iterating between the angle and position estimation steps. We first develop a Data-Aided Maximum a Posteriori (DA- MAP) estimator based on the preamble of the transmitted signal. DA-MAP estimator relies on the knowledge of the transmitted signal which makes it impractical for narrowband communications where the preamble is short. For this reason, a Non-Data- Aided Maximum a Posteriori (NDA-MAP) estimator is developed to improve the AoA accuracy. The iterative positioning algorithms are therefore classified as Data-Aided Iterative (DA-It) and Non-Data-Aided Iterative (NDA-It) depending on the knowledge of the transmitted signal that is used for estimation. Both numerical and experimental analyses are carried out to evaluate the performance of the proposed algorithms. The results show that DA-MAP and NDA-MAP estimators are more accurate than MUSIC. The results also show that DA-It comes very close to the performance of the optimal approach that directly estimates the position based on the observation of the received signal, known as Direct Position Estimation (DPE). Furthermore, the NDA-It algorithm significantly outperforms the DA-It because it can use a much higher number of samples; however, it needs more iterations to converge. In addition, we evaluate the computational savings achieved by the iterative schemes compared to DPE through a detailed complexity analysis. Finally, we investigate the performance degradation of the proposed iterative algorithms due to the impact of multipath and NLOS propagation in indoor environments. Therefore, we develop an enhanced iterative positioning algorithm with an anchor selection method in order to identify and exclude NLOS anchors. The numerical results show that applying the anchor selection strategy significantly improves the positioning accuracy in indoor environments. / Doctorat en Sciences de l'ingénieur et technologie / info:eu-repo/semantics/nonPublished